A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureus
Cell-based mathematical models have previously been developed to simulate the immune system in response to pathogens. Mathematical modeling papers which study the human immune response to pathogens have predicted concentrations of a variety of cells, including activated and resting macrophages, plas...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-11-01
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| Series: | Frontiers in Cellular and Infection Microbiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcimb.2021.711153/full |
| _version_ | 1818838198300704768 |
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| author | Kian Talaei Kian Talaei Kian Talaei Steven A. Garan Steven A. Garan Barbara de Melo Quintela Mette S. Olufsen Joshua Cho Joshua Cho Joshua Cho Julia R. Jahansooz Julia R. Jahansooz Puneet K. Bhullar Puneet K. Bhullar Elliott K. Suen Elliott K. Suen Walter J. Piszker Walter J. Piszker Nuno R. B. Martins Matheus Avila Moreira de Paula Rodrigo Weber dos Santos Marcelo Lobosco |
| author_facet | Kian Talaei Kian Talaei Kian Talaei Steven A. Garan Steven A. Garan Barbara de Melo Quintela Mette S. Olufsen Joshua Cho Joshua Cho Joshua Cho Julia R. Jahansooz Julia R. Jahansooz Puneet K. Bhullar Puneet K. Bhullar Elliott K. Suen Elliott K. Suen Walter J. Piszker Walter J. Piszker Nuno R. B. Martins Matheus Avila Moreira de Paula Rodrigo Weber dos Santos Marcelo Lobosco |
| author_sort | Kian Talaei |
| collection | DOAJ |
| description | Cell-based mathematical models have previously been developed to simulate the immune system in response to pathogens. Mathematical modeling papers which study the human immune response to pathogens have predicted concentrations of a variety of cells, including activated and resting macrophages, plasma cells, and antibodies. This study aims to create a comprehensive mathematical model that can predict cytokine levels in response to a gram-positive bacterium, S. aureus by coupling previous models. To accomplish this, the cytokines Tumor Necrosis Factor Alpha (TNF-α), Interleukin 6 (IL-6), Interleukin 8 (IL-8), and Interleukin 10 (IL-10) are included to quantify the relationship between cytokine release from macrophages and the concentration of the pathogen, S. aureus, ex vivo. Partial differential equations (PDEs) are used to model cellular response and ordinary differential equations (ODEs) are used to model cytokine response, and interactions between both components produce a more robust and more complete systems-level understanding of immune activation. In the coupled cellular and cytokine model outlined in this paper, a low concentration of S. aureus is used to stimulate the measured cellular response and cytokine expression. Results show that our cellular activation and cytokine expression model characterizing septic conditions can predict ex vivo mechanisms in response to gram-negative and gram-positive bacteria. Our simulations provide new insights into how the human immune system responds to infections from different pathogens. Novel applications of these insights help in the development of more powerful tools and protocols in infection biology. |
| first_indexed | 2024-12-19T03:34:35Z |
| format | Article |
| id | doaj.art-e5739fc564e3458c8d31f09fe87aec56 |
| institution | Directory Open Access Journal |
| issn | 2235-2988 |
| language | English |
| last_indexed | 2024-12-19T03:34:35Z |
| publishDate | 2021-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cellular and Infection Microbiology |
| spelling | doaj.art-e5739fc564e3458c8d31f09fe87aec562022-12-21T20:37:25ZengFrontiers Media S.A.Frontiers in Cellular and Infection Microbiology2235-29882021-11-011110.3389/fcimb.2021.711153711153A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureusKian Talaei0Kian Talaei1Kian Talaei2Steven A. Garan3Steven A. Garan4Barbara de Melo Quintela5Mette S. Olufsen6Joshua Cho7Joshua Cho8Joshua Cho9Julia R. Jahansooz10Julia R. Jahansooz11Puneet K. Bhullar12Puneet K. Bhullar13Elliott K. Suen14Elliott K. Suen15Walter J. Piszker16Walter J. Piszker17Nuno R. B. Martins18Matheus Avila Moreira de Paula19Rodrigo Weber dos Santos20Marcelo Lobosco21Center for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United StatesLawrence Berkeley National Laboratory, Berkeley, CA, United StatesDepartment of Integrative Biology, University of California, Berkeley, Berkeley, CA, United StatesCenter for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United StatesLawrence Berkeley National Laboratory, Berkeley, CA, United StatesDepartment of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, BrazilDepartment of Mathematics, North Carolina State University, Raleigh, NC, United StatesCenter for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United StatesLawrence Berkeley National Laboratory, Berkeley, CA, United StatesCollege of Chemistry, University of California, Berkeley, Berkeley, CA, United StatesCenter for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United StatesDepartment of Integrative Biology, University of California, Berkeley, Berkeley, CA, United StatesCenter for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United StatesMayo Clinic Alix School of Medicine, Scottsdale, AZ, United StatesCenter for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United StatesDepartment of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, United StatesCenter for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United StatesCollege of Chemistry, University of California, Berkeley, Berkeley, CA, United StatesCenter for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United StatesDepartment of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, BrazilDepartment of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, BrazilDepartment of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, BrazilCell-based mathematical models have previously been developed to simulate the immune system in response to pathogens. Mathematical modeling papers which study the human immune response to pathogens have predicted concentrations of a variety of cells, including activated and resting macrophages, plasma cells, and antibodies. This study aims to create a comprehensive mathematical model that can predict cytokine levels in response to a gram-positive bacterium, S. aureus by coupling previous models. To accomplish this, the cytokines Tumor Necrosis Factor Alpha (TNF-α), Interleukin 6 (IL-6), Interleukin 8 (IL-8), and Interleukin 10 (IL-10) are included to quantify the relationship between cytokine release from macrophages and the concentration of the pathogen, S. aureus, ex vivo. Partial differential equations (PDEs) are used to model cellular response and ordinary differential equations (ODEs) are used to model cytokine response, and interactions between both components produce a more robust and more complete systems-level understanding of immune activation. In the coupled cellular and cytokine model outlined in this paper, a low concentration of S. aureus is used to stimulate the measured cellular response and cytokine expression. Results show that our cellular activation and cytokine expression model characterizing septic conditions can predict ex vivo mechanisms in response to gram-negative and gram-positive bacteria. Our simulations provide new insights into how the human immune system responds to infections from different pathogens. Novel applications of these insights help in the development of more powerful tools and protocols in infection biology.https://www.frontiersin.org/articles/10.3389/fcimb.2021.711153/fullcytokinesmathematical modelingimmune responseimmune systemStaphycoccus aureuscytokine response |
| spellingShingle | Kian Talaei Kian Talaei Kian Talaei Steven A. Garan Steven A. Garan Barbara de Melo Quintela Mette S. Olufsen Joshua Cho Joshua Cho Joshua Cho Julia R. Jahansooz Julia R. Jahansooz Puneet K. Bhullar Puneet K. Bhullar Elliott K. Suen Elliott K. Suen Walter J. Piszker Walter J. Piszker Nuno R. B. Martins Matheus Avila Moreira de Paula Rodrigo Weber dos Santos Marcelo Lobosco A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureus Frontiers in Cellular and Infection Microbiology cytokines mathematical modeling immune response immune system Staphycoccus aureus cytokine response |
| title | A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureus |
| title_full | A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureus |
| title_fullStr | A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureus |
| title_full_unstemmed | A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureus |
| title_short | A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureus |
| title_sort | mathematical model of the dynamics of cytokine expression and human immune cell activation in response to the pathogen staphylococcus aureus |
| topic | cytokines mathematical modeling immune response immune system Staphycoccus aureus cytokine response |
| url | https://www.frontiersin.org/articles/10.3389/fcimb.2021.711153/full |
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